Evaluation of the Deliciousness of Meat Alternatives

Significance of the Topic

The global demand for meat is rising sharply as the world population approaches 10 billion by 2050. Traditional livestock production contributes significantly to greenhouse gas emissions, land use and water pollution. Plant-based meat (PBM) and cultured meat emerge as sustainable alternatives, replicating the taste, aroma and texture of conventional meat. Reliable analytical methods are essential to evaluate and optimize these products for consumer acceptance and environmental impact.

Objectives and Overview of the Study

This work demonstrates comprehensive analytical approaches to assess the “deliciousness” of meat alternatives. Key goals include:

• Profiling volatile aroma compounds of PBM versus organic beef.
• Classifying meat quality and freshness by machine learning on flavor data.
• Comparing taste-related components such as free amino acids and primary metabolites in PBM and animal meat.
• Quantifying textural properties of meatballs and protein denaturation in chicken.
• Monitoring cell culture media for cultured meat production via metabolite profiling and mechanical testing of cell aggregates.

Applied Methodology and Instrumentation

Multiple analytical platforms were employed:

• SPME-GC-MS with an SPME Arrow for headspace extraction and volatile profiling of cooked PBM and beef.
• Automated GC-MS system (AOC-6000 Plus) and support vector machine (SVM) classification for freshness discrimination.
• Post-column OPA fluorescence derivatization LC for simultaneous quantitation of 18 free amino acids in soy-based and chicken samples.
• UHPLC-MS/MS primary metabolites package for wide-scope profiling (amino acids, organic acids, nucleotides) and principal component analysis (PCA) of ground beef and four PBM products.
• Texture analyzer for shear fracture and compression tests on PBM and chicken meatballs to quantify hardness, cohesiveness and elasticity.
• Differential scanning calorimetry (DSC) to monitor protein denaturation in fried chicken over time.
• LC-MS/MS culture profiling kit for relative quantification of 95 media components (amino acids, vitamins, nucleotides) during hybridoma growth.
• Micro compression testing machine (MCT-510) for mechanical strength measurements of single cell aggregates (HEK293 and iPS cells).

Main Results and Discussion

Flavor Analysis (GC-MS): PBM and organic beef shared key Maillard reaction volatiles but differed in minor compounds due to diverse plant-derived precursors. Machine Learning: SVM classification of volatile profiles achieved 95.8% precision in distinguishing fresh versus deteriorated meat.

Taste Analysis (LC and LC-MS): Free amino acid profiling revealed higher glutamic acid in chicken, correlating with umami. PCA of primary metabolites separated ground beef from PBM samples, reflecting distinct taste signatures.

Texture Analysis: PBM meatballs exhibited greater fracture strength but lower elasticity than chicken analogs. DSC showed actin denaturation completes within 1 hour of heat retention; prolonged holding increased hardness. Compression tests confirmed hardness rise after extended holding.

Cell Culture Monitoring: Glucose and glutamine consumption and lactate accumulation tracked hybridoma growth over five days. Essential nutrients remained stable, enabling process control. Micro-compression tests of cell aggregates yielded reproducible deformation strengths (σ10%) differentiating HEK293 (1.91 MPa) from two iPS lines (1.26 MPa and 1.77 MPa).

Benefits and Practical Applications of the Methods

These integrated techniques offer:

• Objective evaluation of aroma, taste and texture for product development.
• High-throughput freshness screening via automated GC-MS and machine learning.
• Rapid amino acid and metabolite fingerprinting to guide flavor optimization.
• Quantitative texture metrics to improve mouthfeel in meat alternatives.
• Real-time monitoring of cultured meat bioprocesses for quality assurance.

Future Trends and Potential Applications

Emerging directions include combining multi-omics (volatiles, metabolites, proteins) with advanced data analytics and AI for predictive flavor design. Miniaturized, inline sensors could enable continuous process monitoring in cultured meat bioreactors. Enhanced robustness and automation of GC-MS and LC-MS platforms will accelerate QA/QC in industrial production. Integration of mechanical testing and sensory mapping may yield predictive models of consumer acceptance.

Conclusion

A multidisciplinary suite of analytical methods from GC-MS to micro-compression testing provides a robust framework for evaluating the deliciousness and quality of meat alternatives. These approaches support sustainable product innovation, process control in cultured meat manufacturing and consistent sensory performance.

Reference

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